Overview

Dataset statistics

Number of variables21
Number of observations21613
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory168.0 B

Variable types

Numeric17
DateTime1
Categorical3

Warnings

price is highly correlated with bathrooms and 4 other fieldsHigh correlation
bedrooms is highly correlated with bathrooms and 1 other fieldsHigh correlation
bathrooms is highly correlated with price and 7 other fieldsHigh correlation
sqft_living is highly correlated with price and 5 other fieldsHigh correlation
sqft_lot is highly correlated with sqft_lot15High correlation
floors is highly correlated with bathrooms and 1 other fieldsHigh correlation
grade is highly correlated with price and 4 other fieldsHigh correlation
sqft_above is highly correlated with price and 5 other fieldsHigh correlation
yr_built is highly correlated with bathroomsHigh correlation
zipcode is highly correlated with longHigh correlation
long is highly correlated with zipcodeHigh correlation
sqft_living15 is highly correlated with price and 4 other fieldsHigh correlation
sqft_lot15 is highly correlated with sqft_lotHigh correlation
price is highly correlated with sqft_living and 3 other fieldsHigh correlation
bedrooms is highly correlated with bathrooms and 2 other fieldsHigh correlation
bathrooms is highly correlated with bedrooms and 6 other fieldsHigh correlation
sqft_living is highly correlated with price and 5 other fieldsHigh correlation
sqft_lot is highly correlated with sqft_lot15High correlation
floors is highly correlated with bathrooms and 3 other fieldsHigh correlation
grade is highly correlated with price and 6 other fieldsHigh correlation
sqft_above is highly correlated with price and 6 other fieldsHigh correlation
yr_built is highly correlated with bathrooms and 2 other fieldsHigh correlation
zipcode is highly correlated with longHigh correlation
long is highly correlated with zipcodeHigh correlation
sqft_living15 is highly correlated with price and 4 other fieldsHigh correlation
sqft_lot15 is highly correlated with sqft_lotHigh correlation
price is highly correlated with gradeHigh correlation
bedrooms is highly correlated with sqft_livingHigh correlation
bathrooms is highly correlated with sqft_living and 2 other fieldsHigh correlation
sqft_living is highly correlated with bedrooms and 4 other fieldsHigh correlation
sqft_lot is highly correlated with sqft_lot15High correlation
grade is highly correlated with price and 4 other fieldsHigh correlation
sqft_above is highly correlated with bathrooms and 3 other fieldsHigh correlation
sqft_living15 is highly correlated with sqft_living and 2 other fieldsHigh correlation
sqft_lot15 is highly correlated with sqft_lotHigh correlation
yr_built is highly correlated with bathrooms and 4 other fieldsHigh correlation
sqft_lot is highly correlated with sqft_lot15High correlation
lat is highly correlated with zipcodeHigh correlation
grade is highly correlated with bathrooms and 4 other fieldsHigh correlation
bathrooms is highly correlated with yr_built and 7 other fieldsHigh correlation
bedrooms is highly correlated with bathrooms and 2 other fieldsHigh correlation
long is highly correlated with yr_built and 1 other fieldsHigh correlation
zipcode is highly correlated with yr_built and 2 other fieldsHigh correlation
sqft_above is highly correlated with grade and 6 other fieldsHigh correlation
sqft_lot15 is highly correlated with sqft_lotHigh correlation
condition is highly correlated with yr_builtHigh correlation
sqft_basement is highly correlated with bathrooms and 3 other fieldsHigh correlation
sqft_living is highly correlated with grade and 6 other fieldsHigh correlation
floors is highly correlated with yr_builtHigh correlation
sqft_living15 is highly correlated with grade and 4 other fieldsHigh correlation
price is highly correlated with grade and 5 other fieldsHigh correlation
view is highly correlated with waterfrontHigh correlation
waterfront is highly correlated with viewHigh correlation
sqft_basement has 13126 (60.7%) zeros Zeros
yr_renovated has 20699 (95.8%) zeros Zeros

Reproduction

Analysis started2021-06-01 14:35:21.412827
Analysis finished2021-06-01 14:36:27.797988
Duration1 minute and 6.39 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

Distinct21436
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4580301521
Minimum1000102
Maximum9900000190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:28.074153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile512480335
Q12123049194
median3904930410
Q37308900445
95-th percentile9297300429
Maximum9900000190
Range9899000088
Interquartile range (IQR)5185851251

Descriptive statistics

Standard deviation2876565571
Coefficient of variation (CV)0.6280297396
Kurtosis-1.260541871
Mean4580301521
Median Absolute Deviation (MAD)2402530110
Skewness0.2433285476
Sum9.899405677 × 1013
Variance8.274629486 × 1018
MonotonicityNot monotonic
2021-06-01T22:36:28.250648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7950006203
 
< 0.1%
22067002152
 
< 0.1%
6433000402
 
< 0.1%
33330024502
 
< 0.1%
19952002002
 
< 0.1%
17815004352
 
< 0.1%
39041000892
 
< 0.1%
33230590272
 
< 0.1%
63000002262
 
< 0.1%
98090000202
 
< 0.1%
Other values (21426)21592
99.9%
ValueCountFrequency (%)
10001022
< 0.1%
12000191
< 0.1%
12000211
< 0.1%
28000311
< 0.1%
36000571
< 0.1%
36000721
< 0.1%
38000081
< 0.1%
52000871
< 0.1%
62000171
< 0.1%
72000801
< 0.1%
ValueCountFrequency (%)
99000001901
< 0.1%
98950000401
< 0.1%
98423005401
< 0.1%
98423004851
< 0.1%
98423000951
< 0.1%
98423000361
< 0.1%
98393011651
< 0.1%
98393010601
< 0.1%
98393010551
< 0.1%
98393008751
< 0.1%

date
Date

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-27 00:00:00
2021-06-01T22:36:28.416204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:28.581763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4028
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540088.1418
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:28.777275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile210000
Q1321950
median450000
Q3645000
95-th percentile1156480
Maximum7700000
Range7625000
Interquartile range (IQR)323050

Descriptive statistics

Standard deviation367127.1965
Coefficient of variation (CV)0.6797542255
Kurtosis34.58554043
Mean540088.1418
Median Absolute Deviation (MAD)150000
Skewness4.024069145
Sum1.167292501 × 1010
Variance1.347823784 × 1011
MonotonicityNot monotonic
2021-06-01T22:36:28.970723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000172
 
0.8%
350000172
 
0.8%
550000159
 
0.7%
500000152
 
0.7%
425000150
 
0.7%
325000148
 
0.7%
400000145
 
0.7%
375000138
 
0.6%
300000133
 
0.6%
525000131
 
0.6%
Other values (4018)20113
93.1%
ValueCountFrequency (%)
750001
< 0.1%
780001
< 0.1%
800001
< 0.1%
810001
< 0.1%
820001
< 0.1%
825001
< 0.1%
830001
< 0.1%
840001
< 0.1%
850002
< 0.1%
865001
< 0.1%
ValueCountFrequency (%)
77000001
< 0.1%
70625001
< 0.1%
68850001
< 0.1%
55700001
< 0.1%
53500001
< 0.1%
53000001
< 0.1%
51108001
< 0.1%
46680001
< 0.1%
45000001
< 0.1%
44890001
< 0.1%

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.370841623
Minimum0
Maximum33
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:29.153267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9300618311
Coefficient of variation (CV)0.2759138325
Kurtosis49.06365318
Mean3.370841623
Median Absolute Deviation (MAD)1
Skewness1.974299535
Sum72854
Variance0.8650150098
MonotonicityNot monotonic
2021-06-01T22:36:29.287907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
39824
45.5%
46882
31.8%
22760
 
12.8%
51601
 
7.4%
6272
 
1.3%
1199
 
0.9%
738
 
0.2%
813
 
0.1%
013
 
0.1%
96
 
< 0.1%
Other values (3)5
 
< 0.1%
ValueCountFrequency (%)
013
 
0.1%
1199
 
0.9%
22760
 
12.8%
39824
45.5%
46882
31.8%
51601
 
7.4%
6272
 
1.3%
738
 
0.2%
813
 
0.1%
96
 
< 0.1%
ValueCountFrequency (%)
331
 
< 0.1%
111
 
< 0.1%
103
 
< 0.1%
96
 
< 0.1%
813
 
0.1%
738
 
0.2%
6272
 
1.3%
51601
 
7.4%
46882
31.8%
39824
45.5%

bathrooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.114757322
Minimum0
Maximum8
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:29.434515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7701631572
Coefficient of variation (CV)0.3641851238
Kurtosis1.279902444
Mean2.114757322
Median Absolute Deviation (MAD)0.5
Skewness0.5111075733
Sum45706.25
Variance0.5931512887
MonotonicityNot monotonic
2021-06-01T22:36:29.585117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.55380
24.9%
13852
17.8%
1.753048
14.1%
2.252047
 
9.5%
21930
 
8.9%
1.51446
 
6.7%
2.751185
 
5.5%
3753
 
3.5%
3.5731
 
3.4%
3.25589
 
2.7%
Other values (20)652
 
3.0%
ValueCountFrequency (%)
010
 
< 0.1%
0.54
 
< 0.1%
0.7572
 
0.3%
13852
17.8%
1.259
 
< 0.1%
1.51446
 
6.7%
1.753048
14.1%
21930
 
8.9%
2.252047
 
9.5%
2.55380
24.9%
ValueCountFrequency (%)
82
 
< 0.1%
7.751
 
< 0.1%
7.51
 
< 0.1%
6.752
 
< 0.1%
6.52
 
< 0.1%
6.252
 
< 0.1%
66
< 0.1%
5.754
 
< 0.1%
5.510
< 0.1%
5.2513
0.1%

sqft_living
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2079.899736
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:29.752634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11427
median1910
Q32550
95-th percentile3760
Maximum13540
Range13250
Interquartile range (IQR)1123

Descriptive statistics

Standard deviation918.440897
Coefficient of variation (CV)0.4415794093
Kurtosis5.24309299
Mean2079.899736
Median Absolute Deviation (MAD)540
Skewness1.471555427
Sum44952873
Variance843533.6814
MonotonicityNot monotonic
2021-06-01T22:36:29.944157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300138
 
0.6%
1400135
 
0.6%
1440133
 
0.6%
1010129
 
0.6%
1660129
 
0.6%
1800129
 
0.6%
1820128
 
0.6%
1480125
 
0.6%
1720125
 
0.6%
1540124
 
0.6%
Other values (1028)20318
94.0%
ValueCountFrequency (%)
2901
< 0.1%
3701
< 0.1%
3801
< 0.1%
3841
< 0.1%
3902
< 0.1%
4101
< 0.1%
4202
< 0.1%
4301
< 0.1%
4401
< 0.1%
4601
< 0.1%
ValueCountFrequency (%)
135401
< 0.1%
120501
< 0.1%
100401
< 0.1%
98901
< 0.1%
96401
< 0.1%
92001
< 0.1%
86701
< 0.1%
80201
< 0.1%
80101
< 0.1%
80001
< 0.1%

sqft_lot
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9782
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15106.96757
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:30.136606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800
Q15040
median7618
Q310688
95-th percentile43339.2
Maximum1651359
Range1650839
Interquartile range (IQR)5648

Descriptive statistics

Standard deviation41420.51152
Coefficient of variation (CV)2.741815082
Kurtosis285.0778197
Mean15106.96757
Median Absolute Deviation (MAD)2618
Skewness13.06001896
Sum326506890
Variance1715658774
MonotonicityNot monotonic
2021-06-01T22:36:30.318154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000358
 
1.7%
6000290
 
1.3%
4000251
 
1.2%
7200220
 
1.0%
4800120
 
0.6%
7500119
 
0.6%
4500114
 
0.5%
8400111
 
0.5%
9600109
 
0.5%
3600103
 
0.5%
Other values (9772)19818
91.7%
ValueCountFrequency (%)
5201
< 0.1%
5721
< 0.1%
6001
< 0.1%
6091
< 0.1%
6351
< 0.1%
6381
< 0.1%
6492
< 0.1%
6511
< 0.1%
6751
< 0.1%
6761
< 0.1%
ValueCountFrequency (%)
16513591
< 0.1%
11647941
< 0.1%
10742181
< 0.1%
10240681
< 0.1%
9829981
< 0.1%
9822781
< 0.1%
9204231
< 0.1%
8816541
< 0.1%
8712002
< 0.1%
8433091
< 0.1%

floors
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.494308981
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:30.489662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5399888951
Coefficient of variation (CV)0.361363615
Kurtosis-0.4847229368
Mean1.494308981
Median Absolute Deviation (MAD)0.5
Skewness0.6161767212
Sum32296.5
Variance0.2915880069
MonotonicityNot monotonic
2021-06-01T22:36:30.616325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
110680
49.4%
28241
38.1%
1.51910
 
8.8%
3613
 
2.8%
2.5161
 
0.7%
3.58
 
< 0.1%
ValueCountFrequency (%)
110680
49.4%
1.51910
 
8.8%
28241
38.1%
2.5161
 
0.7%
3613
 
2.8%
3.58
 
< 0.1%
ValueCountFrequency (%)
3.58
 
< 0.1%
3613
 
2.8%
2.5161
 
0.7%
28241
38.1%
1.51910
 
8.8%
110680
49.4%

waterfront
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
21450 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Length

2021-06-01T22:36:30.915525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T22:36:31.047193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21613
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common21613
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII21613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
021450
99.2%
1163
 
0.8%

view
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
19489 
2
 
963
3
 
510
1
 
332
4
 
319

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Length

2021-06-01T22:36:31.322454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T22:36:31.413250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring characters

ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21613
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common21613
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII21613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
019489
90.2%
2963
 
4.5%
3510
 
2.4%
1332
 
1.5%
4319
 
1.5%

condition
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
3
14031 
4
5679 
5
1701 
2
 
172
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Length

2021-06-01T22:36:31.704506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T22:36:31.796298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring characters

ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21613
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common21613
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII21613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
314031
64.9%
45679
26.3%
51701
 
7.9%
2172
 
0.8%
130
 
0.1%

grade
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.656873178
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:31.931934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.175458757
Coefficient of variation (CV)0.1535168116
Kurtosis1.190932077
Mean7.656873178
Median Absolute Deviation (MAD)1
Skewness0.7711032008
Sum165488
Variance1.381703289
MonotonicityNot monotonic
2021-06-01T22:36:32.064569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
78981
41.6%
86068
28.1%
92615
 
12.1%
62038
 
9.4%
101134
 
5.2%
11399
 
1.8%
5242
 
1.1%
1290
 
0.4%
429
 
0.1%
1313
 
0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
33
 
< 0.1%
429
 
0.1%
5242
 
1.1%
62038
 
9.4%
78981
41.6%
86068
28.1%
92615
 
12.1%
101134
 
5.2%
11399
 
1.8%
ValueCountFrequency (%)
1313
 
0.1%
1290
 
0.4%
11399
 
1.8%
101134
 
5.2%
92615
 
12.1%
86068
28.1%
78981
41.6%
62038
 
9.4%
5242
 
1.1%
429
 
0.1%

sqft_above
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct946
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.390691
Minimum290
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:32.215189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11190
median1560
Q32210
95-th percentile3400
Maximum9410
Range9120
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation828.0909777
Coefficient of variation (CV)0.4630369538
Kurtosis3.402303621
Mean1788.390691
Median Absolute Deviation (MAD)450
Skewness1.446664473
Sum38652488
Variance685734.6673
MonotonicityNot monotonic
2021-06-01T22:36:32.377711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300212
 
1.0%
1010210
 
1.0%
1200206
 
1.0%
1220192
 
0.9%
1140184
 
0.9%
1400180
 
0.8%
1060178
 
0.8%
1180177
 
0.8%
1340176
 
0.8%
1250174
 
0.8%
Other values (936)19724
91.3%
ValueCountFrequency (%)
2901
< 0.1%
3701
< 0.1%
3801
< 0.1%
3841
< 0.1%
3902
< 0.1%
4101
< 0.1%
4202
< 0.1%
4301
< 0.1%
4401
< 0.1%
4601
< 0.1%
ValueCountFrequency (%)
94101
< 0.1%
88601
< 0.1%
85701
< 0.1%
80201
< 0.1%
78801
< 0.1%
78501
< 0.1%
76801
< 0.1%
74201
< 0.1%
73201
< 0.1%
67201
< 0.1%

sqft_basement
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.5090455
Minimum0
Maximum4820
Zeros13126
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:32.939240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.5750427
Coefficient of variation (CV)1.518220616
Kurtosis2.715574211
Mean291.5090455
Median Absolute Deviation (MAD)0
Skewness1.577965056
Sum6300385
Variance195872.6684
MonotonicityNot monotonic
2021-06-01T22:36:33.146691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013126
60.7%
600221
 
1.0%
700218
 
1.0%
500214
 
1.0%
800206
 
1.0%
400184
 
0.9%
1000149
 
0.7%
900144
 
0.7%
300142
 
0.7%
200108
 
0.5%
Other values (296)6901
31.9%
ValueCountFrequency (%)
013126
60.7%
102
 
< 0.1%
201
 
< 0.1%
404
 
< 0.1%
5011
 
0.1%
6010
 
< 0.1%
651
 
< 0.1%
707
 
< 0.1%
8020
 
0.1%
9021
 
0.1%
ValueCountFrequency (%)
48201
< 0.1%
41301
< 0.1%
35001
< 0.1%
34801
< 0.1%
32601
< 0.1%
30001
< 0.1%
28501
< 0.1%
28101
< 0.1%
27301
< 0.1%
27201
< 0.1%

yr_built
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.005136
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:33.327173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.3734108
Coefficient of variation (CV)0.01490275711
Kurtosis-0.6574075047
Mean1971.005136
Median Absolute Deviation (MAD)23
Skewness-0.4698053988
Sum42599334
Variance862.7972622
MonotonicityNot monotonic
2021-06-01T22:36:33.517661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014559
 
2.6%
2006454
 
2.1%
2005450
 
2.1%
2004433
 
2.0%
2003422
 
2.0%
2007417
 
1.9%
1977417
 
1.9%
1978387
 
1.8%
1968381
 
1.8%
2008367
 
1.7%
Other values (106)17326
80.2%
ValueCountFrequency (%)
190087
0.4%
190129
 
0.1%
190227
 
0.1%
190346
0.2%
190445
0.2%
190574
0.3%
190692
0.4%
190765
0.3%
190886
0.4%
190994
0.4%
ValueCountFrequency (%)
201538
 
0.2%
2014559
2.6%
2013201
 
0.9%
2012170
 
0.8%
2011130
 
0.6%
2010143
 
0.7%
2009230
1.1%
2008367
1.7%
2007417
1.9%
2006454
2.1%

yr_renovated
Real number (ℝ≥0)

ZEROS

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.4022579
Minimum0
Maximum2015
Zeros20699
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:33.715191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation401.67924
Coefficient of variation (CV)4.759105384
Kurtosis18.70115212
Mean84.4022579
Median Absolute Deviation (MAD)0
Skewness4.549493367
Sum1824186
Variance161346.2119
MonotonicityNot monotonic
2021-06-01T22:36:33.894682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020699
95.8%
201491
 
0.4%
201337
 
0.2%
200336
 
0.2%
200035
 
0.2%
200735
 
0.2%
200535
 
0.2%
200426
 
0.1%
199025
 
0.1%
200624
 
0.1%
Other values (60)570
 
2.6%
ValueCountFrequency (%)
020699
95.8%
19341
 
< 0.1%
19402
 
< 0.1%
19441
 
< 0.1%
19453
 
< 0.1%
19462
 
< 0.1%
19481
 
< 0.1%
19502
 
< 0.1%
19511
 
< 0.1%
19533
 
< 0.1%
ValueCountFrequency (%)
201516
 
0.1%
201491
0.4%
201337
0.2%
201211
 
0.1%
201113
 
0.1%
201018
 
0.1%
200922
 
0.1%
200818
 
0.1%
200735
 
0.2%
200624
 
0.1%

zipcode
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.9398
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:34.102162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.50502626
Coefficient of variation (CV)0.0005455357888
Kurtosis-0.8534788732
Mean98077.9398
Median Absolute Deviation (MAD)42
Skewness0.4056612082
Sum2119758513
Variance2862.787835
MonotonicityNot monotonic
2021-06-01T22:36:34.290654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103602
 
2.8%
98038590
 
2.7%
98115583
 
2.7%
98052574
 
2.7%
98117553
 
2.6%
98042548
 
2.5%
98034545
 
2.5%
98118508
 
2.4%
98023499
 
2.3%
98006498
 
2.3%
Other values (60)16113
74.6%
ValueCountFrequency (%)
98001362
1.7%
98002199
 
0.9%
98003280
1.3%
98004317
1.5%
98005168
 
0.8%
98006498
2.3%
98007141
 
0.7%
98008283
1.3%
98010100
 
0.5%
98011195
 
0.9%
ValueCountFrequency (%)
98199317
1.5%
98198280
1.3%
98188136
 
0.6%
98178262
1.2%
98177255
1.2%
98168269
1.2%
98166254
1.2%
98155446
2.1%
9814857
 
0.3%
98146288
1.3%

lat
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5034
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.56005252
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:34.499063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.471
median47.5718
Q347.678
95-th percentile47.74964
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.207

Descriptive statistics

Standard deviation0.1385637102
Coefficient of variation (CV)0.002913447377
Kurtosis-0.6763130016
Mean47.56005252
Median Absolute Deviation (MAD)0.1049
Skewness-0.4852704765
Sum1027915.415
Variance0.0191999018
MonotonicityNot monotonic
2021-06-01T22:36:34.654694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.549117
 
0.1%
47.684617
 
0.1%
47.662417
 
0.1%
47.532217
 
0.1%
47.671116
 
0.1%
47.688616
 
0.1%
47.695516
 
0.1%
47.68615
 
0.1%
47.664715
 
0.1%
47.690415
 
0.1%
Other values (5024)21452
99.3%
ValueCountFrequency (%)
47.15591
< 0.1%
47.15931
< 0.1%
47.16221
< 0.1%
47.16471
< 0.1%
47.17641
< 0.1%
47.17751
< 0.1%
47.17762
< 0.1%
47.17951
< 0.1%
47.18031
< 0.1%
47.18081
< 0.1%
ValueCountFrequency (%)
47.77763
< 0.1%
47.77753
< 0.1%
47.77741
 
< 0.1%
47.77723
< 0.1%
47.77712
 
< 0.1%
47.7772
 
< 0.1%
47.77693
< 0.1%
47.77682
 
< 0.1%
47.77676
< 0.1%
47.77664
< 0.1%

long
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2138964
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21613
Negative (%)100.0%
Memory size169.0 KiB
2021-06-01T22:36:34.841185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.125
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.1408283424
Coefficient of variation (CV)-0.001152310388
Kurtosis1.049500887
Mean-122.2138964
Median Absolute Deviation (MAD)0.101
Skewness0.8850529834
Sum-2641408.943
Variance0.01983262202
MonotonicityNot monotonic
2021-06-01T22:36:35.018707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29116
 
0.5%
-122.3111
 
0.5%
-122.362104
 
0.5%
-122.291100
 
0.5%
-122.37299
 
0.5%
-122.36399
 
0.5%
-122.28898
 
0.5%
-122.35796
 
0.4%
-122.28495
 
0.4%
-122.36594
 
0.4%
Other values (742)20601
95.3%
ValueCountFrequency (%)
-122.5191
 
< 0.1%
-122.5151
 
< 0.1%
-122.5141
 
< 0.1%
-122.5121
 
< 0.1%
-122.5112
< 0.1%
-122.5092
< 0.1%
-122.5071
 
< 0.1%
-122.5061
 
< 0.1%
-122.5053
< 0.1%
-122.5042
< 0.1%
ValueCountFrequency (%)
-121.3152
< 0.1%
-121.3161
< 0.1%
-121.3191
< 0.1%
-121.3211
< 0.1%
-121.3251
< 0.1%
-121.3522
< 0.1%
-121.3591
< 0.1%
-121.3642
< 0.1%
-121.4021
< 0.1%
-121.4031
< 0.1%

sqft_living15
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.552492
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:35.200226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.3913043
Coefficient of variation (CV)0.3450154512
Kurtosis1.59709581
Mean1986.552492
Median Absolute Deviation (MAD)410
Skewness1.108181276
Sum42935359
Variance469761.2399
MonotonicityNot monotonic
2021-06-01T22:36:35.358766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540197
 
0.9%
1440195
 
0.9%
1560192
 
0.9%
1500181
 
0.8%
1460169
 
0.8%
1580167
 
0.8%
1610166
 
0.8%
1800166
 
0.8%
1720166
 
0.8%
1620165
 
0.8%
Other values (767)19849
91.8%
ValueCountFrequency (%)
3991
 
< 0.1%
4602
 
< 0.1%
6202
 
< 0.1%
6701
 
< 0.1%
6902
 
< 0.1%
7002
 
< 0.1%
7102
 
< 0.1%
7202
 
< 0.1%
7408
< 0.1%
7503
 
< 0.1%
ValueCountFrequency (%)
62101
 
< 0.1%
61101
 
< 0.1%
57906
< 0.1%
56101
 
< 0.1%
56001
 
< 0.1%
55001
 
< 0.1%
53801
 
< 0.1%
53401
 
< 0.1%
53301
 
< 0.1%
52201
 
< 0.1%

sqft_lot15
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8689
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12768.45565
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2021-06-01T22:36:35.542275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1999.2
Q15100
median7620
Q310083
95-th percentile37062.8
Maximum871200
Range870549
Interquartile range (IQR)4983

Descriptive statistics

Standard deviation27304.17963
Coefficient of variation (CV)2.138408933
Kurtosis150.76311
Mean12768.45565
Median Absolute Deviation (MAD)2505
Skewness9.506743247
Sum275964632
Variance745518225.3
MonotonicityNot monotonic
2021-06-01T22:36:35.700888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000427
 
2.0%
4000357
 
1.7%
6000289
 
1.3%
7200211
 
1.0%
4800145
 
0.7%
7500142
 
0.7%
8400116
 
0.5%
3600111
 
0.5%
4500111
 
0.5%
5100109
 
0.5%
Other values (8679)19595
90.7%
ValueCountFrequency (%)
6511
 
< 0.1%
6591
 
< 0.1%
6601
 
< 0.1%
7482
< 0.1%
7504
< 0.1%
7551
 
< 0.1%
7571
 
< 0.1%
7581
 
< 0.1%
7881
 
< 0.1%
7941
 
< 0.1%
ValueCountFrequency (%)
8712001
< 0.1%
8581321
< 0.1%
5606171
< 0.1%
4382131
< 0.1%
4347281
< 0.1%
4255811
< 0.1%
4229671
< 0.1%
4119621
< 0.1%
3920402
< 0.1%
3868121
< 0.1%

Interactions

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2021-06-01T22:36:08.618045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-06-01T22:36:09.022007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:09.219510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:09.426927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:09.611378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:09.801869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:10.015331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:10.198841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:10.388336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:10.582782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:10.781167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:10.962748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:11.145455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:11.350935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:11.545508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:11.725655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:11.931106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:12.132686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:12.338170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:12.518684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:12.699928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:12.883393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:13.051941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:13.239440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:13.408606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:13.599088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:13.780636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:13.952181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:14.148620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:14.334121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:14.507690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:14.676210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:14.853768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:15.031262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:15.228739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:15.402300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:15.571860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:15.766331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:15.927864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:16.089432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:16.277927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:16.456482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:16.629780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:16.803315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:16.969871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:17.155375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:17.320982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:17.482500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:17.645066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:18.113858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:18.296369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:18.482384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:18.660913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:18.817494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:18.986082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:19.155624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:19.319155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:19.514631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:19.679192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:19.870738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:20.034242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:20.204834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:20.371374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:20.538893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:20.699573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:20.870117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:21.052630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:21.232151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:21.414722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:21.581154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:21.755655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:21.914276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:22.087767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:22.248338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:22.412716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:22.594229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:22.765282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:22.922859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:23.098391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:23.257963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:23.411387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:23.553008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:23.708595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:23.873152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:24.044694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:24.231243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:24.386780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:24.565348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T22:36:24.721931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-06-01T22:36:35.912728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-01T22:36:36.350826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-01T22:36:36.720835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-01T22:36:37.106807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-01T22:36:37.434894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-01T22:36:25.068960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-01T22:36:25.770082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
071293005202014-10-13221900.031.00118056501.0003711800195509817847.5112-122.25713405650
164141001922014-12-09538000.032.25257072422.000372170400195119919812547.7210-122.31916907639
256315004002015-02-25180000.021.00770100001.000367700193309802847.7379-122.23327208062
324872008752014-12-09604000.043.00196050001.000571050910196509813647.5208-122.39313605000
419544005102015-02-18510000.032.00168080801.0003816800198709807447.6168-122.04518007503
572375503102014-05-121225000.044.5054201019301.00031138901530200109805347.6561-122.0054760101930
613214000602014-06-27257500.032.25171568192.0003717150199509800347.3097-122.32722386819
720080002702015-01-15291850.031.50106097111.0003710600196309819847.4095-122.31516509711
824146001262015-04-15229500.031.00178074701.000371050730196009814647.5123-122.33717808113
937935001602015-03-12323000.032.50189065602.0003718900200309803847.3684-122.03123907570

Last rows

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
2160378521400402014-08-25507250.032.50227055362.0003822700200309806547.5389-121.88122705731
2160498342013672015-01-26429000.032.00149011263.0003814900201409814447.5699-122.28814001230
2160534489002102014-10-14610685.042.50252060232.0003925200201409805647.5137-122.16725206023
2160679360004292015-03-261007500.043.50351072002.000392600910200909813647.5537-122.39820506200
2160729978000212015-02-19475000.032.50131012942.000381180130200809811647.5773-122.40913301265
216082630000182014-05-21360000.032.50153011313.0003815300200909810347.6993-122.34615301509
2160966000601202015-02-23400000.042.50231058132.0003823100201409814647.5107-122.36218307200
2161015233001412014-06-23402101.020.75102013502.0003710200200909814447.5944-122.29910202007
216112913101002015-01-16400000.032.50160023882.0003816000200409802747.5345-122.06914101287
2161215233001572014-10-15325000.020.75102010762.0003710200200809814447.5941-122.29910201357